{"id":"W2904324595","doi":"10.1016/j.biortech.2018.12.024","title":"Hydrothermal pretreatment of source separated organics for enhanced solubilization and biomethane recovery","year":2018,"lang":"en","type":"article","venue":"Bioresource Technology","topic":"Anaerobic Digestion and Biogas Production","field":"Engineering","cited_by":38,"is_retracted":false,"has_abstract":false,"ca_institutions":"Greenfield Research (Canada); Toronto Metropolitan University","funders":"Ontario Water Consortium","keywords":"Biogas; Anaerobic digestion; Mesophile; Chemistry; Solubilization; Yield (engineering); Hydrothermal circulation; Methane; Pulp and paper industry; Nuclear chemistry; Waste management; Materials science; Chemical engineering; Biochemistry; Organic chemistry; Bacteria; Biology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00009024493,0.0002217119,0.0001796362,0.0001512801,0.0001307513,0.000229496,0.0001382533,0.0001909533,0.0008147139],"category_scores_gemma":[0.0001354084,0.0001140806,0.0001859328,0.0001975667,0.0001154972,0.0002477799,0.0001662636,0.0003248444,0.0001895682],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002655058,"about_ca_system_score_gemma":0.000293928,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001130823,"about_ca_topic_score_gemma":0.003866068,"domain_scores_codex":[0.9999393,0.000005175961,0.000005630886,0.00001134989,0.00001904539,0.00001948388],"domain_scores_gemma":[0.9999661,0.000007177891,0.00000844339,0.000003928482,0.00000742995,0.000006951519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0000374457,0.000009483896,0.00006042638,0.00001853605,0.000003155177,0.00002521455,0.000009566716,0.00009677293,0.9985076,0.00004713561,0.00001318439,0.001171319],"study_design_scores_gemma":[0.00000400331,0.00002688199,0.000724995,0.000001359607,0.0000044503,0.00001847767,0.0000115575,0.000391979,0.9982882,0.0000200353,0.0005052959,0.000002760868],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937196,0.000455878,0.004535296,0.00005740324,0.00002417623,0.00001575924,0.000154991,0.00003352343,0.00100342],"genre_scores_gemma":[0.9955673,0.0003599512,0.002323974,0.00001188237,0.000006849839,0.000008092787,0.0001285105,0.00001256072,0.001580772],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.001130823,"threshold_uncertainty_score":0.002725482,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.005735323621473477,"score_gpt":0.2112893161246239,"score_spread":0.2055539925031505,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}